A desirable diabetes management and treatment includes a combined continuous blood glucose monitoring and insulin delivery system that operate autonomously. In such systems, control software would monitor the output of the continuous blood glucose monitor and calculate appropriate delivery instructions for the insulin delivery system. Such a system is often referred to as closed loop blood glucose control and the control software is often referred to as a closed loop blood glucose control algorithm.
The development of a closed loop blood glucose control algorithm is the most challenging aspect of the development of a closed loop blood glucose control system. This challenge arises from complicated features of diabetes management such as noise and delays that are inherent features of blood glucose monitoring and insulin delivery. Another complicating aspect of developing a closed loop blood glucose control algorithm is that individuals can differ significantly in the details of their lifestyle (e.g., diet, activity level) and in the details of their physiology (e.g., size, fitness, insulin sensitivity).
Furthermore, failure of a closed loop blood glucose control algorithm could potentially have lethal consequences. Thus a closed loop blood glucose control algorithm will need to be comprehensively tested and likely need to be tuned or personalized to each individual user.
Currently, testing a closed loop blood glucose control algorithm requires the use of a living diabetic subject or a mathematical model of a diabetic subject. The living subject can be animal or human. Testing a closed loop blood glucose control algorithm on a living subject suffers from the disadvantage that such testing is expensive, time consuming and poses significant risks to the health of the test subject. Testing a closed loop blood glucose control algorithm with a mathematical model of a diabetic subject suffers from the fact that human physiology is far too complex to be sufficiently represented by any currently available mathematical model. The main advantage of the method described herein is that it is fast, inexpensive and incurs no risk and also captures the inherent complexity of a live diabetic subject.